Hybrid Template Update System for Unimodal Biometric Systems

Provided by: Cornell University
Topic: Security
Format: PDF
Semi-supervised template update systems allow to automatically taking into account the intra-class variability of the biometric data over time. Such systems can be inefficient by including too many impostor's samples or skipping too many genuine's samples. In the first case, the biometric reference drifts from the real biometric data and attracts more often impostors. In the second case, the biometric reference does not evolve quickly enough and also progressively drifts from the real biometric data. The authors propose a hybrid system using several biometric sub-references in order to increase performance of self-update systems by reducing the previously cited errors. The proposition is validated for a keystroke dynamics authentication system (this modality suffers of high variability over time) on two consequent datasets from the state of the art.

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